Image based spam blocking

ABSTRACT

A fingerprint of an image identified within a received message is generated following analysis of the message. A spam detection engine identifies an image within a message and converts the image into a grey scale image. The spam detection engine analyzes the grey scale image and assigns a score. A fingerprint of the grey scale image is generated based on the score. The fingerprint may also be based on other factors such as the message sender&#39;s status (e.g. blacklisted or whitelisted) and other scores and reports generated by the spam detection engine. The fingerprint is then used to filter future incoming messages.

CROSS-REFERENCE TO RELATED APPLICATIONS

The present application is a continuation and claims the prioritybenefit of U.S. patent application Ser. No. 12/715,335 filed Mar. 1,2010, which is a continuation and claims the priority benefit of U.S.patent application Ser. No. 12/651,299 filed Dec. 31, 2009, which claimsthe priority benefit of U.S. provisional application 61/142,108 filedDec. 31, 2008 and U.S. provisional application 61/228,620 filed Jul. 27,2009, the disclosures of which are incorporated herein by reference.

BACKGROUND OF THE INVENTION

1. Field of the Invention

The present invention generally relates to image based spam blocking.More particularly, the present invention concerns generating afingerprint for an image located within an image based electronic-mailmessage.

2. Description of the Related Art

Electronic-mail (email) security and maintaining that security occurs ina hostile environment. Spammers are constantly attempting to “beat thesystem” by coming up with new ways to avoid a determination that theirmessage is spam. Spam detection is often based on the content of amessage, for example, offers for a Rolex.®. watch or sales of Viagra.®.pills. By detecting messages that repeatedly use the terms Rolex orViagra and/or those terms in the context of other information such as aphone number or email address, a determination might be made that amessage is spam.

This determination is often made through parsing of the text of themessage and identifying key words or words suggestive of spam content.These methods are not always effective and may result in a falsepositive that a message is spam. For example, a message might be anexchange between medical professionals concerning Viagra or a buyerpurchasing a Rolex.®. watch from a seller via an online auction orshopping website such as eBay.com or Amazon.com.

Spammers have now begun to embed their spam messages (e.g. disruptive,unwanted, or unsolicited messages) in an image (image based spam). Imagebased spam is a message where the text is embedded in an image, whichmakes the message more difficult to parse than text or ASCII basedmessages. In some instances, a spammer will prepare a message, take ascreen shot of the image, and embed the image in the message without anysurrounding ASCII or text. Since there is no text to parse in the imagebased spam message, every word the spammer intends to convey remainsillustrated in the image. Traditional spam detection techniques orfilters cannot effectively detect or stop these image based spammessages.

Optical character recognition (OCR) may be used to identify words in amessage. Similar to pure text-based parsing techniques, the words in themessage are parsed and a determination is made as to whether the messageis spam. OCR is, however, slow, computationally intensive, and easilyfooled. For example, by rotating an image by only a few degrees where aline of text in an image now appears on a slant, the OCR recognitionsoftware may require additional computational cycles thereby delayingprocessing and/or result in incorrect character recognition, which maylead to failed recognition of the message as spam all together.

Spammers may also insert random noise such as dots or other backgroundartifacts. Noise and artifacts make it difficult for OCR recognitiontechniques to identify the lines of text that may encompass a spammessage. The human eye may process the content of the message withoutany issue, but computer imaging and processing techniques may notoperate as well in the OCR context with such noise present.

Traditional spam filters or spam detection methods that assess thecontent of a message are thus proving to be ineffective against imagebased messages. There is a need for a context insensitive messagedetection technique that effectively detects and blocks image based spammessages.

SUMMARY OF THE PRESENTLY CLAIMED INVENTION

In a first claimed embodiment, a method for filtering an image basedmessage is claimed. Through the method, an image based message isreceived from a sender. An image located within the received image basedmessage is converted into a grey scale image, which is then analyzed toidentify one or more text lines associated with the grey scaled image. Ascore is assigned to the grey scale image based on the presence orabsence of a text line within the grey scale image. The image basedmessage is then filtered based on the assigned score.

In a second claimed embodiment, a system for filtering an image basedmessage is claimed. The system includes a processor that executesinstructions, a spam detection engine executable by the processor, andmemory. Execution of the spam detection engine by the processor convertsan image located within the image based message into a grey scale image,analyzes the grey scale image to identify one or more text linesassociated with the grey scale image, assigns a score to the grey scaleimage based on the presence or absence of a text line within the greyscale image, and filters the image based message based on the assignedscore. The memory stores the aforementioned detection engine, grey scaleimage and assigned score.

In a third claimed embodiment, a computer-readable storage medium isclaimed. The storage medium includes a computer program that isexecutable by a processor to perform a method for filtering an imagebased message. The method includes conversion of an image located withinan image based message from a sender into a grey scale image, analysisof the grey scale image to identify one or more text lines associatedwith the grey scale image, assignment of a score to the grey scale imagewhich is based on the presence of absence of a text line within the greyscale mage, and filtering of the image in the image based message basedon the assigned score.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 illustrates an overview diagram of a spam detection system usedto develop a fingerprint of an image based message.

FIG. 2 illustrates a block diagram of the spam detection engine of FIG.1.

FIG. 3 is a flowchart illustrating a method for filtering a receivedimage based message based on an assigned score.

FIG. 4 is a flow chart illustrating a method for generating afingerprint of an image in an image based message.

FIG. 5 illustrates a computing system that may be used to implement anembodiment of the present invention.

DETAILED DESCRIPTION

FIG. 1 illustrates an overview diagram of a spam detection system 100used to develop a fingerprint of an image based message. The spamdetection system 100 of FIG. 1 includes clients 110-120, an image basedmessage 130, network 140, spam detection engine 150, and fingerprintlibrary 160.

Spam detection system 100 may comprise a plurality of clients. Clients110 and 120 are inclusive of a general purpose computing device capableof accessing information over a network like the device illustrated inFIG. 5. Clients 110 and 120 may be implemented as computing devices suchas workstations, servers, lap top computers, mobile devices, or othercomputing devices that can communicate over network 140. Clients 110 and120 include software and/or hardware capable of sending and receivingmessages.

Client 110, a message sender, may generate a message based on inputreceived from a user. A message may include an email message (e.g.spam), instant message, text message, and/or any other electronicallytransmitted information. Client 110 may send a message over network 140to client 120, a message recipient. Network 140 is inclusive of anycommunication network such as the Internet, Wide Area Network (WAN),Local Area Network (LAN), intranet, extranet, private network, or othernetwork.

Spam detection engine 150 and its various components may be stored inmemory and is executable by a processor of a network computing device toanalyze a message 130 and determine whether the message 130 is a goodmessage (solicited or wanted message) or spam message. The spamdetection process and analysis is discussed in more detail in FIGS. 3-4below. Spam detection engine 150 and the corresponding network computingdevice may be a stand-alone software and/or hardware component asillustrated in FIG. 1 and located behind a firewall. The spam detectionengine 150 and corresponding network device may be an integratedenterprise system or integrated directly with client 120. Spam detectionengine 150 may also be used in addition to or in connection with otherspam detection services or software systems such as email filteringprograms or server-side email filters.

In an exemplary embodiment of the present invention, an image embeddedin an image based message is extracted from the message 130. The imageis converted into a grey scale message and assigned a score by the spamdetection engine 150. The message 130 may then be appropriatelyprocessed based on the score (e.g., delivered, deleted, or quarantined).

In other embodiments, a fingerprint is generated based in part on thescore assigned to the grey scale image. A generated fingerprint may bestored in fingerprint library 160 for future use in identifying anincoming message as spam. In some embodiments, the contents offingerprint library 160 may be shared amongst users of network 140 on alocal or global scale. Fingerprint library 160 may be part of a databaseof the network device hosting the spam detection engine 150.

FIG. 2 illustrates a block diagram 200 of the spam detection engine 150of FIG. 1. Spam detection engine 150 is executable by a processingdevice to process and detect spam messages using one or more automatedand/or manual message categorization, classification, filtering, and/oranti-spam techniques. Spam detection engine 150, which is stored inmemory of the network device of FIG. 1 or an appropriate computingdevice, includes various modules such as filter 210, classifier 220,challenge/response 230, and fingerprint generator 240. Message 130 maybe processed by any one component of spam detection engine 150 or anycombination thereof. Spam detection engine 150 may by executable by aprocessing device to access and store data in a database such asfingerprint library 160. The database storing such a library 160 may bestored in memory of computing device

Filter 210 is executable to process and organize a message based onspecified rules or criteria and may be used to distinguish between agood message and spam message. Filter 210 may use any filteringtechnique known in the art such as country-based filtering,checksum-based filtering, hybrid filtering, or rule-based filteringtechniques (e.g., content filtering or heading filtering). Filter 210may also employ various DNS-based blacklists or other DNS-basedanti-spam systems to block unwanted messages. A filtered message may bechanged prior to delivery, blocked, deleted, redirected, or delivered tothe client 120. Although a single filter is shown in FIG. 2, one or morefilters may be used in other embodiments and the number of filters mayvary for different implementations.

Classifier 220 is executable to process and classify a message 130 as aspam message, a good message, or some other category. Classifier 220parses a message 130 to obtain various features associated with themessage. Following parsing, a score based in part on whether the messageis a good message or spam message is automatically calculated and usedto classify the message 130. The score may also be based on a userinput. For example, a user may indicate whether a particular message isa good message or spam message or whether a message sender should beblacklisted or whitelisted. Following processing or messageclassification by classifier 220, information relating to the classifiedmessage (e.g., a score) is stored in a database. The classifier 220 maybe updated with the scores associated with particular messages for usein future classification of incoming messages. In one embodiment,classifier 220 utilizes a statistical filtering technique such asBayesian filtering.

Classification of a message as a good message or spam message may alsodepend on whether message sender is listed as a valid sender(whitelisted) or invalid sender (blacklisted). A status of a messagesender being whitelisted or blacklisted may be sufficient in classifyinga message and authorizing delivery of the message 130 to client 120.Alternatively, message sender status may be one factor along with otherfactors to consider when classifying the message 130. The informationrelating to the classified message (e.g., score, message sender status)may also be used or considered by fingerprint generator 240 indeveloping a fingerprint of the message 130 as described in more detailbelow.

Classifier 220 may be a reliable statistical classifier like astatistical message classifier disclosed in U.S. patent publicationnumber 2008/0097946, the disclosure of which is incorporated herein byreference. The reliable statistical classifier may be a whitelistclassifier, collaborative fingerprinting classifier, an image analyzer,a probe account, a challenge response classifier, or any otherclassifier known in the art. A reliable good classifier or reliable junkclassifier may be used while in other embodiments, the same classifiermay classify both good and junk messages and may be machine classifiers,user-augmented classifiers, or a statistical classifier. Although asingle classifier is shown in FIG. 2, one or more classifiers may beused in other embodiments and the number of classifiers may vary fordifferent implementations. Classification may be improved by usingmultiple classifiers. A classified message may be changed prior todelivery, blocked, deleted, redirected, or delivered to the client 120.

Challenge/response module 230 processes an incoming message bysubjecting the message or message sender to a test or series of tests.Passing the test allows delivery of the sender message.Challenge/response module 230 may test all incoming messages or onlytest the messages believed to be spam messages. Challenge/responsemodule 230 may use human answerable or machine answerablechallenge/response processes or any combination thereof like thosedescribed in U.S. patent application number 2008-0104188 A1 for “MessageAuditing,” the disclosure of which is incorporated herein by reference.

In some embodiments, the message sender (client 110) may have specialsoftware or hardware that enables it to automatically answer a challengequestion and send its answer to an auditor such as a third-party server)that independently verifies the answer. As a result, the auditorgenerates and sends a report to the message recipient (client 120). Thereport may include the status of the message sender's address and/orinformation regarding whether the message sender's (client 110) answerto the challenge is correct.

Challenge/response module 230 may receive the report from the auditorand determine whether the message 130 should be delivered to the messagerecipient (client 120). The report derived from the challenge/responseprocess may be saved in a database for the processing of future incomingmessages. The report may also be used or considered by fingerprintgenerator 240 in developing a fingerprint for the message 130 asdescribed in more detail below. Although a single challenge/responsemodule is shown in FIG. 2, one or more challenge/response modules orsystems may be used.

Fingerprint generator module 240 is executable to develop a fingerprintor spam profile of a message to aid in the processing of spam messages.After receiving a message 130 from client 110 via network 140,fingerprint generator 240 determines if the message 130 includes animage. If fingerprint generator 240 identifies an image within themessage, the image is extracted from the message 130 and converted to agrey scale image using image processing software or any other techniqueknown in the art. Conversion to a grey scale image eliminates backgroundnoise that can interfere with text detection since spammers oftenattempt to evade or deceive traditional text detection methods such asOCR by making text blurry or adding background noise. The use of a greyscale image with the present invention bypasses this possible impedimentby focusing the analysis on the intensity of color.

After receiving a message 130 from client 110 and identifying any imagespresent within the image, fingerprint generator 240 analyzes informationabout the content of the message 130 rather than content within themessage. In one embodiment, fingerprint generator 240 uses edgedetection or some other type of image processing or image analysistechnique known in the art to identify any text lines, characters oftext, and/or boundaries associated with the characters and theidentified image.

Character boundaries, for example, may indicate the existence of textsince a line of text tends to be uniform (much like a news article,headline, or normal message) through a particular line although the sizeand configuration of certain lines may change character throughout thecourse of a message. Thus, by identifying lines of text regardless ofsize, fingerprint generator 240 also identifies characteristics of thetext that can be used to characterize or profile a message. For example,fingerprint generator 240 may detect and determine that five or moretext lines within an image may be indicative of a spam message whereas asingle line is not (e.g. a single line may be a caption or bannerassociated with a photo).

Fingerprint generator 240 may detect columns of text rather than lines.Fingerprint generator 240 may further or alternatively detect patternsof characters or words. For example, fingerprint generator 240 maydetect the pattern or presence of 10characters and 2 spaces followed by6 characters and the pattern of 10 characters followed by 6 characters.Fingerprint generator 240 may find that the 10-2-6 pattern is indicativeof a spam message whereas the 10-6 pattern is not. In another example,fingerprint generator 240 may identify and determine that a time/datestamp within an image (e.g. in the lower right hand corner) isacceptable and indicative of a good message where several lines of textin the lower right hand corner of an image is not.

Generally, an image based spam message contains more lines of text thana normal image such as a family photo or an image of outdoor scenery,and text lines often have higher contrast and more intensity than nontext areas. In one embodiment, fingerprint generator 240 creates ahistogram using the results of edge detection analysis. The histogramcompares the number of non white pixels to the number of white pixelsfound in the grey scale image. Fingerprint generator 240 can identifygroupings of pixels based on high contrast and intensity.

Fingerprint generator 240 may use the results of the histogram (e.g.groupings) to determine the presence or absence of text lines orcharacters appearing within the message 130. Fingerprint generator 240then develops a fingerprint or spam profile based on characteristics ofthe received message such as the presence or absence of text lines orcharacters identified within an image of an image based message, or thenumber of text lines or characters identified (e.g. histogram). Thegenerated fingerprint may be stored in fingerprint library 160 forfuture comparison and detection of incoming messages as spam messages.

The generated fingerprint may be based on the results of edge detectionanalysis alone or may also be based on other factors such as the dataderived from filter 210, classifier 220, and challenge/response system230. For example, fingerprint generator 240 may base a fingerprint onthe message sender's status (e.g. blacklisted or whitelisted), the scoreassigned to a classified message, and/or the report generated followinga successful or unsuccessful message challenge response process.

Fingerprint generator 240 determines the number of text lines associatedwith a particular image and stores that data in a database. For example,after collecting text line information for various images, fingerprintgenerator 240 can use that data to build a normal distribution of goodimages and spam images versus test lines. Fingerprint generator 240 cancalculate mean and standard deviation values for numbers of text linesin good images and spam messages. Thus when a newly received image isanalyzed, the number of text lines found within that image can becompared with the mean and standard deviation values for previous imagesstored in the database to help determine whether the newly receivedimage is a spam message.

In one embodiment, fingerprint generator 240 may consider thisinformation (i.e., whether the newly received image is a good message orspam message) when developing a fingerprint for the image.Alternatively, this information may be used to filter incoming messagesusing filter 210.

FIG. 3 is a flowchart 300 illustrating a method for filtering a receivedimage based message based on an assigned score. The steps of the processof FIG. 3 may be embodied in hardware or software including acomputer-readable storage medium comprising instructions executable by aprocessor of a computing device. At step 305, spam detection engine 150receives an incoming message 130 from client 110 via network 140. Atstep 310, spam detection engine 150 determines if an image is present inmessage 130. If an image is not present, spam detection engine 150processes the message at step 315. For example, the message 130 isfiltered by filter 210, classified by classifier 220, and/or subjectedto a challenge/response process by challenge/response module 230 asdescribed above. Following processing by spam detection engine 150,message 130 is classified as a good message or spam message at step 320.If the message is classified as a good message, the message is deliveredto the message recipient at step 325. If the message is classified as aspam message, then delivery to the message recipient is blocked at step330. Alternatively, message 130 may be classified in another appropriatecategory (not shown) or redirected for further processing by spamdetection engine 150.

Returning to step 310, if an image is present within the receivedmessage, fingerprint generator 240 extracts the image from the messageand converts the image into a grey scale image for further processingand analysis at step 335. At step 340, using edge detection techniquesor other image processing or image analysis techniques known in the art,fingerprint generator 240 analyzes the grey scale image to identify thepresence or absence of text lines or characters, characters of text,and/or boundaries associated with the characters in the identifiedimage.

Fingerprint generator 240 may also identify columns of text and patternsof characters or words. Based on the presence or absence of text linesor characters and the numbers of text lines or characters identifiedwithin the image, fingerprint generator 240 assigns a score to the imageat step 345. For example, a more negative score or a score less thanzero may be indicative of a spam message. The score may also be based onother factors or data stored in memory and accessible by the spamdetection engine 150 such as the message sender's status (e.g.blacklisted or whitelisted), a score assigned to a classified message,and/or the report generated following a successful or unsuccessfulmessage challenge response process. The assigned score may be saved to adatabase 250 for future use and processing of messages (not shown). Atstep 350, filter 210 filters the message 130 based on the assignedscore. Following filtering of the message 130, the message 130 may bechanged prior to delivery, blocked, deleted, redirected, or delivered tothe client 120 (not shown).

FIG. 4 is a flow chart 400 illustrating a method for generating afingerprint of an image in an image based message. The steps of theprocess of FIG. 4 may be embodied in hardware or software including amachine-readable medium comprising instructions executable by a machinesuch as a processor of a computing device. At step 405, spam detectionengine 150 receives an incoming message 130 from client 110 via network140. At step 410, spam detection engine 150 determines if an image ispresent in message 130. If an image is not present, spam detectionengine 150 processes the message at step 415. For example, the message130 is filtered by filter 210 and/or classified by classifier 220 asdescribed above. The message 130 may be subjected to achallenge/response process.

Following processing by spam detection engine 150, message 130 isclassified as a good message or spam message at step 420. If the messageis classified as a good message, the message is delivered to the messagerecipient at step 425. If the message is classified as a spam message,then delivery to the message recipient is blocked at step 430.Alternatively, message 130 may be classified in another appropriatecategory or redirected for further processing by spam detection engine150.

Returning to step 410, if an image is present within the receivedmessage, fingerprint generator 240 extracts the image and converts theimage into a grey scale image for further processing and analysis atstep 435. At step 440, using edge detection techniques or other imageprocessing or image analysis techniques known in the art, fingerprintgenerator 240 analyzes the grey scale image to identify the presence orabsence of text lines or characters, characters of text, and/orboundaries associated with the characters in the identified image.Fingerprint generator 240 may also identify columns of text and patternsof characters or words.

Based on the presence or absence of text lines or characters and thenumbers of text lines or characters identified within the image,fingerprint generator 240 assigns a score to the image at step 445. Forexample, a more negative score or a score less than zero may beindicative of a spam message. The score may also be based on otherfactors or data stored in memory and accessible by the spam detectionengine 150 such as the message sender's status (e.g. blacklisted orwhitelisted), a score assigned to a classified message, and/or thereport generated following a successful or unsuccessful messagechallenge response process.

The score may also be based on other factors or data stored in memoryand accessible by the spam detection engine 150 such as the messagesender's status (e.g. blacklisted or whitelisted), the score assigned toa classified message, and/or the report generated following a successfulor unsuccessful message challenge response process. The assigned scoremay be saved to a database for future use and processing of messages(not shown).

At step 450, fingerprint generator 240 generates a fingerprint for thegrey scale image. The generated fingerprint may be based on the resultsof edge detection analysis alone (e.g. assigned score) or may also bebased on other factors or data such as the message sender's status (e.g.blacklisted or whitelisted), the score assigned to a classified message,and/or the report generated following a successful or unsuccessfulmessage challenge response process. Following fingerprint generation,filter 210 may filter the message 130 based on the generated fingerprint(not shown). The filtered message 130 may be changed prior to delivery,blocked, deleted, redirected, or delivered to the client 120.

FIG. 5 illustrates a computing system that may be used to implement anembodiment of the present invention. System 500 of FIG. 5 may beimplemented in the context of the system of FIG. 1. The computing system500 of FIG. 5 includes one or more processors 510 and memory 520. Mainmemory 520 stores, in part, instructions and data for execution byprocessor 510. Main memory 520 can store the executable code when inoperation. Main memory 520 also includes a database that may house afingerprint library 160 (see FIG. 2). The database in main memory 520may also store various data such as filtering rules or criteria, a scoreassociated with a message 130, message sender status (e.g. valid orinvalid, blacklisted or whitelisted, etc.), and a report generated froma message challenge/response process. The system 500 of FIG. 5 furtherincludes a mass storage device 530, portable storage medium drive(s)540, output devices 550, user input devices 560, a graphics display 570,and peripheral devices 580.

The components shown in FIG. 5 are depicted as being connected via asingle bus 590. The components, however, may be connected through one ormore data transport means. For example, processor unit 510 and mainmemory 520 may be connected via a local microprocessor bus, and the massstorage device 530, peripheral device(s) 580, portable storage device540, and display system 570 may be connected via one or moreinput/output (I/O) buses.

Mass storage device 530, which may be implemented with a magnetic diskdrive or an optical disk drive, is a non-volatile storage device forstoring data and instructions for use by processor unit 510. Massstorage device 530 can store the system software for implementingembodiments of the present invention for purposes of loading softwareinto main memory 520.

Portable storage device 540 operates in conjunction with a portablenon-volatile storage medium, such as a floppy disk, compact disk orDigital video disc, to input and output data and code to and from thecomputer system 500 of FIG. 5. The system software for implementingembodiments of the present invention may be stored on such a portablemedium and input to the computer system 500 via the portable storagedevice 540.

Input devices 560 provide a portion of a user interface. Input devices560 may include an alpha-numeric keypad, such as a keyboard, forinputting alpha-numeric and other information, or a pointing device,such as a mouse, a trackball, stylus, or cursor direction keys.Additionally, the system 500 as shown in FIG. 5 includes output devices550. Examples of suitable output devices include speakers, printers,network interfaces, and monitors.

Display system 570 may include a liquid crystal display (LCD) or othersuitable display device. Display system 570 receives textual andgraphical information, and processes the information for output to thedisplay device.

Peripherals 580 may include any type of computer support device to addadditional functionality to the computer system. For example, peripheraldevice(s) 580 may include a modem or a router.

The components contained in the computer system 500 of FIG. 5 are thosetypically found in computer systems that may be suitable for use withembodiments of the present invention and are intended to represent abroad category of such computer components that are well known in theart. Thus, the computer system 500 of FIG. 5 can be a personal computer,hand held computing device, telephone, mobile computing device,workstation, server, minicomputer, mainframe computer, or any othercomputing device. The computer can also include different busconfigurations, networked platforms, multi-processor platforms, etc.Various operating systems can be used including Unix, Linux, Windows,Macintosh OS, Palm OS, and other suitable operating systems.

The foregoing detailed description of the technology herein has beenpresented for purposes of illustration and description. It is notintended to be exhaustive or to limit the technology to the precise formdisclosed. Many modifications and variations are possible in light ofthe above teaching. The described embodiments were chosen in order tobest explain the principles of the technology and its practicalapplication to thereby enable others skilled in the art to best utilizethe technology in various embodiments and with various modifications asare suited to the particular use contemplated. It is intended that thescope of the technology be defined by the claims appended hereto.

1. A method for filtering image-based messages, the method comprising:receiving an image-based message from a sender; and executinginstructions store d in memory, wherein execution of the instructions bya processor: identifies one or more text lines within an image of theimage-based message, determines whether the one or more text lines fit apredetermined numeric pattern of characters and spaces, assigns a scoreto the message based on whether the text lines fit the numeric pattern,and filters the received image-based message based on the assignedscore.
 2. The method of claim 1, further comprising converting the imageinto a grey-scale image.
 3. The method of claim 1, further comprisingdetermining a number of the text lines, wherein assigning the score isfurther based on the determined number of text lines.
 4. The method ofclaim 1, further comprising identifying one or more columns of text,wherein assigning the score is further based on the identified columnsof text.
 5. The method of claim 1, wherein assigning the score isfurther based on a location of the identified text lines within theimage-based message.
 6. The method of claim 5, further comprisingdetermining that the text lines comprise a date and/or time, wherein theassigned score based on the date and/or time indicates that the messageis likely to be good.
 7. The method of claim 1, further comprisingcreating a histogram regarding pixel color.
 8. The method of claim 7,further comprising identifying groupings of pixels based on contrastand/or intensity.
 9. The method of claim 1, further comprisinggenerating a profile of the image-based message based on the identifiedtext lines.
 10. The method of claim 9, further comprising storing thegenerated profile in a library in memory with other profiles, whereinthe stored profiles are available for comparison with subsequentincoming messages.
 11. A system for filtering image-based messages, thesystem comprising: a communication interface for receiving animage-based message from a sender; and a processor for executinginstructions store d in memory, wherein execution of the instructions bythe processor: identifies one or more text lines within an image of theimage-based message, determines whether the one or more text lines fit apredetermined numeric pattern of characters and spaces, assigns a scoreto the message based on whether the text lines fit the numeric pattern,and filters the received image-based message based on the assignedscore.
 12. The system of claim 11, wherein the processor executesfurther instructions to convert the image into a grey-scale image. 13.The system of claim 11, wherein the processor executes furtherinstructions to determine a number of the text lines, wherein the scoreis further based on the determined number of text lines.
 14. The systemof claim 11, wherein the processor executes further instructions toidentify one or more columns of text, wherein the score is further basedon the identified columns of text.
 15. The system of claim 11, whereinthe score is further based on a location of the identified text lineswithin the image-based message.
 16. The system of claim 15, wherein theprocessor executes further instructions to determine that the text linescomprise a date and/or time, wherein the assigned score based on thedate and/or time indicates that the message is likely to be good. 17.The system of claim 11, wherein the processor executes furtherinstructions to creates a histogram regarding pixel color.
 18. Thesystem of claim 17, wherein the processor executes further instructionsto identify groupings of pixels based on contrast and/or intensity. 19.The system of claim 11, wherein the processor executes furtherinstructions to generate a profile of the image-based message based onthe identified text lines.
 20. The system of claim 19, furthercomprising a library in memory for storing the generated profile withother profiles, wherein the stored profiles are available for comparisonwith subsequent incoming messages.
 21. A non-transitorycomputer-readable storage medium, having embodied thereon a programexecutable by a processor to perform a method for filtering image-basedmessages, the method comprising: receiving an image-based message from asender; identifying one or more text lines within an image of theimage-based message; determining whether the one or more text lines fita predetermined numeric pattern of characters and spaces; assigning ascore to the message based on whether the text lines fit the numericpattern, and filtering the received image-based message based on theassigned score.